Semantic Gateway as a Service architecture for IoT Interoperability

The Internet of Things (IoT) is set to occupy a substantial component of future Internet. The IoT connects sensors and devices that record physical observations to applications and services of the Internet. As a successor to technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has stumbled into vertical silos of proprietary systems, providing little or no interoperability with similar systems. As the IoT represents future state of the Internet, an intelligent and scalable architecture is required to provide connectivity between these silos, enabling discovery of physical sensors and interpretation of messages between things. This paper proposes a gateway and Semantic Web enabled IoT architecture to provide interoperability between systems using established communication and data standards. The Semantic Gateway as Service (SGS) allows translation between messaging protocols such as XMPP, CoAP and MQTT via a multi-protocol proxy architecture. Utilization of broadly accepted specifications such as W3C's Semantic Sensor Network (SSN) ontology for semantic annotations of sensor data provide semantic interoperability between messages and support semantic reasoning to obtain higher-level actionable knowledge from low-level sensor data.Comment: 16 page

How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

Challenges of Creating a Knowledge-Based Society: Education & Research for India & Gujarat

Presented at the World Gujarat Conference, Edison, NJ, August 30, 2008

CS 875: Semantic Web

World Wide Web (Web 1.0, or the Web, as we now know it) centers on documents and semistructured data in html, rss, and xml. The next generation Web, also called Web 2.0 and Web 3.0, has already started to emerge. Web 2.0 is about user-generated content, user participation such as through tagging, and social networking. Web 3.0, also called Semantic Web, is about labeling content such that machines can process it more intelligently and humans can exploit it more effectively. These labels or metadata add semantics (meaning) to data, and their formal representation enables powerful reasoning that leads not only to better (semantic) search but also to analysis, discovery, and decision making. Semantic Web is already a rapidly emerging field, with standards, technologies, products, and applications-as well as to excellent job prospects (for MS students) and research opportunities (for PhD students)

Finding Street Gang Members on Twitter

Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate.Comment: 8 pages, 9 figures, 2 tables, Published as a full paper at 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016

Sparse matrix product implementation on field programmable gate arrays (EPGAS)

If dense matrix multiplication algorithms are used with sparse matrices, they can result in a large number of redundant calculations, as numerous elements in sparse matrices are zero valued, thus available resources and time may be wasted. The algorithm discussed here aims to take advantage of the sparseness of the matrices by multiplying only nonzero elements. The NIOS development board from Altera is used for implementing the above algorithm. First a sequential program in the C programming language is downloaded onto the FPGA and run by the NIOS soft-processor. Then the same board is also used for a parallel implementation of the above algorithm using three NIOS soft-processors within the same FPGA. Such an approach is very critical because current FPGAs do not contain enough resources to solve large problems. For example, we cannot build large memory systems within FPGAs so we need to employ algorithms that have rather limited memory requirements. Our proposed matrix multiplication algorithm for sparse matrices uses the available memory space very cautiously and also results in good execution times. Performance results testify to this fact